What would make AI useful here?
We assess where AI could help your team and plan how to test it with the right information and human checks.
Our role We assess opportunities and readiness, then define a pilot. Training models, connecting systems and building production software are agreed separately.
Explore the services For Business sponsors, CIOs, CDOs & operations leaders
Agent Context Design
Specify what information an AI agent needs at each step of its work.
A context specification showing sources, timing and access limits.
Agent Evaluation Criteria
Agree how to judge an agent before trialling it with users.
A scorecard with test scenarios, acceptable results and reasons to stop the test.
Agent Feedback Loop Design
Plan how people report agent mistakes and who acts on those reports.
A feedback process showing how issues are recorded, assigned and reviewed.
Agent Knowledge Requirements
Work out what an agent needs to know to do the job you have in mind.
A knowledge inventory showing sources, owners and missing information.
Agent Permissions & Boundaries
Decide what an agent can access or do, and when it must ask a person.
Documented permissions, approval points and escalation rules.
Agent Pilot Scoping
Plan a small agent trial around one role and a limited set of actions.
A pilot plan covering users, permissions, supervision, tests and when to stop.
Agent Readiness Assessment
Check whether your information, workflow and controls are ready for the proposed agent.
The gaps to close before a trial and a recommendation for the first test.
Agent Role Definition
Define the job an agent would do and which responsibilities stay with people.
A role description covering allowed tasks, exclusions and handovers.
Agent Workflow Design
Map the steps an agent would follow, including decisions and handovers.
A workflow showing tasks, dependencies and limits on the agent’s actions.
Agentic AI Opportunity Mapping
Look for work that could benefit from an AI agent, and compare it with simpler approaches.
Ranked opportunities and advice on where an agent is worth investigating.
AI Business Case Framing
Work out what an AI project would need to save or improve to justify its cost.
A business-case outline with cost assumptions, expected benefits and evidence still needed.
AI Data Requirements
Identify the data an AI task needs and check whether it can be accessed and used.
A data brief covering sources, quality, access and ownership.
AI Evaluation Frameworks
Agree what counts as a good AI result for the task you have in mind.
Evaluation criteria, test cases and a plan for reviewing results.
AI Opportunity Discovery
Look at the work your team does and where AI might help.
A shortlist of opportunities, with reasons to explore or reject each.
AI Output Failure Analysis
Examine AI mistakes and trace what might be causing them.
An analysis of failure patterns and recommended improvements to test.
AI Output Qualification
Decide what needs checking before someone can use an AI result in the business.
Quality checks and guidance on when a person must review the output.
AI Pilot Definition
Plan a limited AI trial around a specific improvement you want to test.
A pilot brief with responsibilities and criteria for judging the results.
AI Strategy Scoping
Turn a broad interest in AI into a business question you can investigate.
A brief defining the question, expected value and work to be done.
AI Use-Case Discovery
Describe the tasks, users and situations behind a possible AI application.
Use-case briefs showing the inputs, expected outputs and constraints.
AI Use-Case Prioritisation
Compare possible AI uses against their value, practicality and risks.
A ranked shortlist and recommended next tests.
AI Value Assessment
Assess whether the likely benefit of an AI application justifies its cost and complexity.
A value assessment with baseline measures and advice on whether to pursue it.
AI-Assisted Decision Support
Work out how AI could help people gather evidence and compare options for a recurring decision.
A proposed approach with source requirements and human review steps.
AI-Enabled Commercial Workflows
Explore where AI could help with sales preparation, account research or proposals.
A proposed workflow showing the AI tasks, human handovers and measures of value.
AI-Enabled Customer Operations
Assess where AI could help customer-support teams and when a person needs to take over.
Workflow recommendations with knowledge requirements and escalation rules.
AI-Enabled Idea Generation
Plan a way to use AI to explore ideas and question the team’s usual assumptions.
A method with prompts, selection criteria and human review.
AI-Enabled Innovation Processes
Work out where AI could help you find opportunities, compare concepts and test ideas.
A process map showing AI tasks, evidence checks and who is responsible.
AI-Enabled Internal Operations
Find repetitive internal work where AI might reduce effort or delays.
A map of candidate tasks and a plan for testing the benefit.
AI-Enabled Knowledge Work
Assess how AI could help people find, summarise and reuse information in their daily work.
Recommended tasks to test, with source requirements and review checks.
AI-Enabled Product Development
Identify where AI could help a product team research, prioritise or make development decisions.
A proposed workflow with quality checks and measures for a trial.
AI-Enabled Research
Plan how AI can assist research while keeping sources and conclusions traceable.
A research workflow with source standards and verification steps.
AI-Enabled Workflow Redesign
Work out how a process would change if AI took on selected tasks.
A proposed workflow showing what AI does, what people do and how to check the results.
AI-Ready Information Architecture
Organise business information so an AI workflow can find and interpret it.
A proposed structure for content and metadata, with ownership rules.
Build / Buy / Partner Assessment
Compare building a capability yourselves, buying a product or working with a partner.
An options comparison covering fit, cost, control and dependencies.
Business Knowledge Mapping
Find the knowledge and rules people rely on to do the work.
A knowledge map showing reliable sources and gaps.
Business Process Analysis
Follow a process from start to finish to find delays, rework and difficult decisions.
A review of how the process works and where it could improve.
Business Rule Mapping
Document the rules behind recurring actions, including exceptions and disagreements about how to apply them.
A rule register showing conditions, owners and questions still to resolve.
Context & Semantic Architecture
Define the meanings and relationships an AI system needs to understand your business information.
An information model and requirements for the context the system needs.
Data Activation Strategy
Work out how to use available data in a specific decision or workflow.
A plan linking data sources to tasks, owners and measures of value.
Data Opportunity Assessment
Assess where data you have or could obtain would help the business.
Ranked opportunities, with feasibility checks and gaps to investigate.
Data Quality Assessment
Check whether the data is reliable enough for the job you need it to do.
An assessment of quality problems and which to fix first.
Data Readiness Assessment
Check whether your available data can support the AI or business use you have in mind.
A readiness assessment and the data gaps to address.
Data Source Mapping
Find where relevant data lives, who controls it and how it moves between teams or systems.
A source map with access details, update frequency and known limitations.
Data Strategy
Connect your business priorities to the data and capabilities needed to support them.
Data priorities, ownership requirements and a plan for putting them in place.
Decision Mapping
Identify the decisions in a workflow, the information each needs and who makes them.
A map of decisions, responsibilities and escalation points.
Evaluation Dataset Design
Choose test examples that reflect the real task, including cases where mistakes would matter.
A test-set specification and a scoring method.
Exception Mapping
Find the unusual cases a process or AI workflow needs to handle.
A record of exceptions, their likely consequences and proposed handling rules.
Explicit Knowledge Review
Check documented information for gaps, outdated advice and contradictions.
A review showing what needs correcting before the information is reused.
Fine-Tuning Feasibility Assessment
Assess whether a model needs fine-tuning or whether better instructions, sources or workflow would address the problem.
A recommended approach, with data requirements and an evaluation plan.
Human Review & Feedback Design
Decide how people will check AI results and report what needs improving.
Review responsibilities, feedback routes and escalation criteria.
Human–Agent Workflow Design
Work out how people and agents share tasks, check results and handle exceptions.
A workflow with responsibilities, approval points and human handovers.
Information Relationship Mapping
Clarify how business concepts, documents and data connect.
A relationship map and definitions to guide information or technical design.
Institutional Memory Discovery
Find past decisions and lessons that your team struggles to retrieve.
A referenced record of decisions and lessons, with context and knowledge owners.
Knowledge Gap Analysis
Compare what a team or AI task needs to know with the information available.
A list of knowledge gaps, their impact and who could help close them.
Model Behaviour Requirements
Specify what a model should do, avoid or refer to a person for the intended task.
Behaviour requirements and criteria for accepting its results.
Organisational Language / Terminology Mapping
Resolve differences in the names and abbreviations teams use for the same things.
A shared glossary of terms, definitions and unresolved differences.
Prompt Architecture
Organise instructions and context for AI tasks that need to be repeated consistently.
A prompt structure and a plan for checking how well it works.
Retrieval & Knowledge-Grounding Strategy
Work out which sources an AI system needs to consult to answer a question reliably.
Requirements for choosing sources, retrieving information and supporting answers with evidence.
Tacit Knowledge Discovery
Talk to people about the know-how they use but have never written down.
A knowledge map drawn from interviews, including judgement calls and exceptions to check.
Training-Data Requirements
Specify what examples and data a proposed model improvement would need.
A data specification covering quality, coverage and gaps to address.
Workflow Mapping
Map the tasks, tools and handovers in a process before deciding what to change.
A workflow map showing responsibilities and possible points for improvement.
What you’ll
receive.
- 01Tasks where AI could help the business
- 02The information, workflow and human checks those tasks need
- 03A pilot plan with limits and measures of success
What we work with
The task you want to improve, the people who do it and the information available to them.
How we shape the scope
We assess opportunities and readiness, then define a pilot. Training models, connecting systems and building production software are agreed separately.
Tell us what
you’re working on.
We’ll discuss what you need and whether we can help.
Let’s talk